Throughout human history, there has been a need to verify an individual's identity. Signature verification is perhaps one of the oldest means for identifying a particular person. Similar to other biometrical information, such as fingerprint analysis, a signature conveys distinguishable characteristics that indicate personal identity. Given its long usage throughout our culture and the relative lack of privacy concerns when compared to fingerprint and other biometric analysis, accurate and efficient fingerprint analysis remains an important field.
As more individuals must identify themselves in a computing environment, electronic verification of signatures has become increasingly important. However, automatic signature verification is a challenging task due to practical constraints. For example, it is often impractical to collect large amount of signatures of a new user for training purposes. Moreover, it is also impractical and inefficient to get forgeries or “negative samples” when using the system.
Existing methods of signature verification can be classified into two categories: off-line and on-line. Off-line methods acquire data by scanning signatures and process them as static images. On-line methods capture signature tracks in time-variable sequences, such as positions, pressure, and pen tilt. On-line methods usually achieve higher accuracy than off-line ones because they can make use of dynamic information (speed, pressure, etc.) that is missing in static images.
Many attempts have been made to perfect on-line signature verification systems. Dynamic time warping (“DTW”) is one widely-used method to find the similarity between the input signature patterns and the stored templates. The signature pattern is usually represented by a sequence of feature vectors defined on every sample point of the signature. Hidden Markov Model (“HMM”) is another technique for signature verification in recent years, because it has been successful in modeling time-variable sequences for speech and on-line handwriting recognition. Another method, Gaussian Mixture Model (“GMM”) has also been attempted for signature distribution estimation. These models (DTW, HMM, and GMM) focus on local properties of signatures such as local moving direction and shape curvature.
Unfortunately, past attempts have been associated with several drawbacks. Using complex systems such as those required under current protocols, usually requires large training sample sets and processing capabilities. Moreover, as previously mentioned, many systems often require forgeries or negative samples from which to compare. Therefore, there exists a need in the art for electronic systems and methods for accurately and efficiently verifying signatures. Another need exists for a simple system that incorporates the advantages of two or more complex systems without the drawbacks associated with the complex systems. These and other needs are met with one or more aspects of the invention.